KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース)

MicroRNAs (miRNAs) are endogenous ∼23 nt RNAs that play important gene-regulatory roles in animals and plants by pairing to the mRNAs of protein-coding genes to direct their posttranscriptional repression. This review outlines the current understanding of miRNA target recognition in animals and discusses the widespread impact of miRNAs on both the expression and evolution of protein-coding genes.

MicroRNAs: Target Recognition and Regulatory Functions

Epilepsy in adults.

Recent studies have demonstrated the utility and superiority of long non-coding RNAs (lncRNAs) as novel biomarkers for cancer diagnosis, prognosis, and therapy In the present study, the prognostic value of lncRNAs in glioblastoma multiforme was systematically investigated by performing a genome-wide analysis of lncRNA expression profiles in 419 glioblastoma patients from The Cancer Genome Atlas (TCGA) project Using survival analysis and Cox regression model, we identified a set of six lncRNAs (AC0050135, UBE2R2-AS1, ENTPD1-AS1, RP11-89C212, AC0731156, and XLOC_004803) demonstrating an ability to stratify patients into high- and low-risk groups with significantly different survival (median 0899 vs 1611 years, p = 387e−09, log-rank test) in the training cohort The six-lncRNA signature was successfully validated on independent test cohort of 219 patients with glioblastoma, and it revealed superior performance for risk stratification with respect to existing lncRNA-related signatures Multivariate Cox and stratification analysis indicated that the six-lncRNA signature was an independent prognostic factor after adjusting for other clinical covariates Further in silico functional analysis suggested that the six-lncRNA signature may be involved in the immune-related biological processes and pathways which are very well known in the context of glioblastoma tumorigenesis The identified lncRNA signature had important clinical implication for improving outcome prediction and guiding the tailored therapy for glioblastoma patients with further prospective validation

An Immune-Related Six-lncRNA Signature to Improve Prognosis Prediction of Glioblastoma Multiforme

Rheumatoid arthritis (RA) is an autoimmune disease with an unknown etiology, occurring in approximately 1.0% of general population. More and more studies have suggested that long non-coding RNAs (lncRNAs) could play important roles in various biological processes and be associated with the pathogenesis of different kinds of diseases including RA. Although a large number of lncRNAs have been found, our knowledge of their function and physiological/pathological significance is still in its infancy. In order to reveal functional lncRNAs and identify the key lncRNAs in RA, we reconstructed a global triple network based on the competitive endogenous RNA (ceRNA) theory using the data from National Center for Biotechnology Information Gene Expression Omnibus and our previous paper. Meanwhile, Gene Ontology (GO) and pathway analysis were performed using Cytoscape plug-in BinGO and Database for Annotation, Visualization, and Integration Discovery (DAVID), respectively. We found that the lncRNA-miRNA-mRNA network was composed of 7 lncRNA nodes, 90 mRNA nodes, 24 miRNA nodes, and 301 edges. The functional assay showed that 147 GO terms and 23 pathways were enriched. In addition, three lncRNAs (S5645.1, XR_006437.1, J01878) were highly related to RA, and therefore, were selected as key lncRNAs. This study suggests that specific lncRNAs are associated with the development of RA, and three lncRNAs (S5645.1, XR_006437.1, J01878) could be used as potential diagnostic biomarkers and therapeutic targets.

Reconstruction and analysis of the lncRNA-miRNA-mRNA network based on competitive endogenous RNA reveal functional lncRNAs in rheumatoid arthritis.

// Yuze Cao 1 , Peng Wang 2 , Shangwei Ning 2 , Wenbiao Xiao 1 , Bo Xiao 1 , Xia Li 2 1 Department of Neurology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan Province, China 2 College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang Province, China Correspondence to: Xia Li, email: lixia@hrbmu.edu.cn Bo Xiao, email: xiaobo_xy@126.com Keywords: lncRNA, ceRNA network, topology, prognostic biomarker, synergistic regulation Received: March 18, 2016      Accepted: May 10, 2016      Published: May 24, 2016 ABSTRACT Glioblastoma multiforme (GBM) is a highly malignant brain tumor associated with a poor prognosis. Cross-talk between competitive endogenous RNAs (ceRNAs) plays a critical role in tumor development and physiology. In this study, we present a multi-step computational approach to construct a functional GBM long non-coding RNA (lncRNA)-mediated ceRNA network (LMCN) by integrating genome-wide lncRNA and mRNA expression profiles, miRNA-target interactions, functional analyses, and clinical survival analyses. LncRNAs in the LMCN exhibited specific topological features consistent with a regulatory association with coding mRNAs across GBM pathology. We determined that the lncRNA MCM3AP-AS was involved in RNA processing and cell cycle-related functions, and was correlated with patient survival. MCM3AP-AS and MIR17HG acted synergistically to regulate mRNAs in a network module of the competitive LMCN. By integrating the expression profile of this module into a risk model, we stratified GBM patients in both the The Cancer Genome Atlas and an independent GBM dataset into distinct risk groups. Finally, survival analyses demonstrated that the lncRNAs and network module are potential prognostic biomarkers for GBM. Thus, ceRNAs could accelerate biomarker discovery and therapeutic development in GBM.

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Identification of prognostic biomarkers in glioblastoma using a long non-coding RNA-mediated, competitive endogenous RNA network

Temporal lobe epilepsy (TLE) is the most common form of adult epilepsy and frequently evolving drug resistance. Although there is growing consensus that noncoding ribonucleic acids (ncRNAs) are modulators of TLE, the knowledge about the deoxyribonucleic acid (DNA) methylation patterns of ncRNAs in TLE remains limited. In the current study, we constructed DNA methylation profiles from 30 TLE patients and 30 healthy controls for ncRNAs, primarily focusing on long ncRNAs (lncRNAs) and microRNAs (miRNAs), by reannotating data of DNA methylation BeadChip. Statistics analyses have revealed a global hypermethylation pattern in miRNA and lncRNA gene in TLE patients. Bioinformatic analyses have found aberrantly methylated miRNAs and lncRNAs are related to ion channel activity, drug metabolism, mitogen-activated protein kinase (MAPK) signaling pathway, and neurotrophin signaling pathway. Aberrantly methylated ncRNA and pathway target might be involved in TLE development and progression. The methylated and demethylated ncRNAs identified in this study provide novel insights for developing TLE biomarkers and potential therapeutic targets.

Genome-Wide DNA Methylation Patterns Analysis of Noncoding RNAs in Temporal Lobe Epilepsy Patients.

Purpose The aim of this work was to investigate expression and cross-talk between long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) in a rat model of temporal lobe epilepsy (TLE). Methods Noncoding RNA chips were used to explore the expression and relationship between lncRNAs and miRNAs in a rat model of TLE. The expression of different lncRNAs and mRNAs was analysed by Pearson’s correlation coefficient, and the function of each lncRNA was annotated by co-expressed genes based on gene ontology classification using DAVID. MiRNA-lncRNA interactions were predicted by using StarBase v2.0, and the competing endogenous RNA (ceRNA) relationship between lncRNAs and miRNAs was built by using Cytoscape software. Real-time PCR was used to verify chip results. Results According to the expression profile analysis, 54 lncRNAs, 36 miRNAs and 122 mRNAs were dysregulated in TLE rat model compared to normal controls. The functions of lncRNAs in epilepsy were annotated by their co-expressed genes based on the “guilt by association” strategy. DAVID analysis revealed that differentially expressed lncRNA functions were involved in “potassium channel activity”, “metal ion transmembrane transporter activity”, and “voltage-gated potassium channel activity”. Based on the ceRNA theory, 13 mRNAs, 10 miRNAs and 11 lncRNAs comprise the lncRNA-miRNA-mRNA ceRNA relationship in epilepsy. Conclusions The molecular functions of the differentially expressed genes play an important role in the pathogenesis of voltage-gated potassium channel activity. Further ceRNA analyses suggest that modulation of lncRNAs could emerge as a promising therapeutic target for TLE.

Construction and analysis of a dysregulated lncRNA-associated ceRNA network in a rat model of temporal lobe epilepsy.

Temporal lobe epilepsy (TLE) is a complex disease with its pathogenetic mechanism still unclear. Single-nucleotide polymorphisms (SNPs) of miRNA (miRSNPs) are SNPs located on miRNA genes or target sites of miRNAs, which have been proved to be associated with neuropsychic disease development by interfering with miRNA-mediated regulatory function. In this study, we integrated TLE-related risk genes and risk pathways multi-dimensionally based on public data resources. Furthermore, we systematically screened candidate functional miRSNPs for TLE and constructed a TLE-associated pathway-based miRSNP switching network, which included 92 miRNAs that target 12 TLE risk pathways. Moreover, we dissected thoroughly the correlation between 5 risk genes of 4 risk pathways and TLE development. Additionally, the biological function of several candidate miRSNPs were validated by luciferase reporter assay. In silico approach facilitates to select potential "miRSNP-miRNA-risk gene-pathway" axis for experimental validation, which provided new insights into the mechanism of miRSNPs as potential genetic risk factors of TLE.

Network and Pathway-Based Analysis of Single-Nucleotide Polymorphism of miRNA in Temporal Lobe Epilepsy

Diabetes in the elderly increases cognitive impairment, but the underlying mechanisms are still far from fully understood. A non-targeted metabolomics approach based on liquid chromatography-mass spectrometry (LC-MS) was performed to screen out the serum biomarkers of diabetic mild cognitive impairment (DMMCI) in rats. Total 48 SD rats were divided into three groups, Normal control (NC) group, high-fat diet (HFD) fed group and type 2 diabetes mellitus (T2DM) group. The T2DM rat model was induced by intraperitoneal administration of streptozotocin (STZ, 35 mg/kg) after 6 weeks of high-fat diet (HFD) feeding. Then each group was further divided into 4-week and 8-week subgroups, which were calculated from the time point of T2DM rat model establishment. The novel object recognition test (NORT) and the Morris water maze (MWM) method were used to evaluate the cognitive deficits in all groups. Compared to the NC-8w and HFD-8w groups, both NOR and MWM tests indicated significant cognitive dysfunction in the T2DM-8w group, which could be used as an animal model of DMMCI. Serum was ultimately collected from the inferior vena cava after laparotomy. Metabolic profiling analysis was conducted using ultra high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) technology. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to verify the stability of the model. According to variable importance in the project (VIP > 1) and the p-value of t-test (P < 0.05) obtained by the OPLS-DA model, the metabolites with significant differences were screened out as potential biomarkers. In total, we identified 94 differentially expressed (44 up-regulated and 50 down-regulated) endogenous metabolites. The 10 top up-regulated and 10 top down-regulated potential biomarkers were screened according to the FDR significance. These biomarkers by pathway topology analysis were primarily involved in the metabolism of sphingolipid (SP) metabolism, tryptophan (Trp) metabolism, Glycerophospholipid (GP) metabolism, etc. Besides, SP metabolism, Trp metabolism and GP metabolism mainly belonging to the lipid metabolism showed marked perturbations over DMMCI and may contribute to the development of disease. Taken collectively, our results revealed that T2DM could cause cognitive impairment by affecting a variety of metabolic pathways especially lipid metabolism. Besides, serum PE, PC, L-Trp, and S1P may be used as the most critical biomarkers for the early diagnosis of DMMCI.

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Wenbiao Xiao

Papers

Identification of prognostic biomarkers in glioblastoma using a long non-coding RNA-mediated, competitive endogenous RNA network

Genome-Wide DNA Methylation Patterns Analysis of Noncoding RNAs in Temporal Lobe Epilepsy Patients.

Construction and analysis of a dysregulated lncRNA-associated ceRNA network in a rat model of temporal lobe epilepsy.

Network and Pathway-Based Analysis of Single-Nucleotide Polymorphism of miRNA in Temporal Lobe Epilepsy

LC-MS-Based Untargeted Metabolomics Reveals Early Biomarkers in STZ-Induced Diabetic Rats With Cognitive Impairment